Welcome and Chapter 1
Welcome!
Instructor
- Dr. Tyler George: tgeorge@cornellcollege.edu
Course logistics
- Course Dates: August 26th to September 18th
- Course sessions: M-F,9am-11am and 1pm-3pm
- Exam Dates: September 6th and 18th
Generalized Linear Models
In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
. . .
Logistic regression
\[\begin{aligned}\pi = P(y = 1 | x) \hspace{2mm} &\Rightarrow \hspace{2mm} \text{Link function: } \log\big(\frac{\pi}{1-\pi}\big) \\ &\Rightarrow \log\big(\frac{\pi}{1-\pi}\big) = \beta_0 + \beta_1~x\end{aligned}\]
What we’re covering this semester(1/3)
Generalized Linear Models (Ch 1 - 6)
- Introduce models for non-normal response variables
- Estimation, interpretation, and inference
- Mathematical details showing how GLMs are connected
What we’re covering this semester(2/3)
Modeling correlated data (Ch 7 - 9)
- Introduce multilevel models for correlated and longitudinal data
- Estimation, interpretation, and inference
- Mathematical details, particularly diving into covariance structures
What we’re covering this semester(3/3)
More Regression Models (ITSL Chapter 7) - Polynomial Regression - Regression Splines - Smoothing Splines - Generalized Additive Models (GAMS)
Meet your classmates!
- Create larger groups
- Quick introductions - Name, year, and major
- Choose a reporter
- Need help choosing? Person with birthday closest to December 1st.
- Identify 8 things everyone in the group has in common
- Not being a Cornell Student
- Not clothes (we’re all wearing socks)
- Not body parts (we all have a nose)
. . .
Reporter will share list with the class
What background is assumed for the course?
. . .
Pre-reqs
- STA 201, 202 and DSC 223
. . .
Background knowledge
- Statistical content
- Linear and logistic regression
- Statistical inference
- Basic understanding of random variables
- Computing
- Using R for data analysis
- Writing reports using R Markdown or Quarto
Course Toolkit (1/2)
- Website
- https://stats-tgeorge.github.io/STA363_AdvReg/
- Central hub for the course
- Notes
- Labs
- Datasets
Course Toolkit (1/2)
- Moodle:
- https://moodle.cornellcollege.edu/course/view.php?id=7908
- Submissions
- Gradebook
- Announcements
Class Meetings
Lectures
- Some traditional lecture
- Individual and group labs
- Bring fully-charged laptop
- Mini-projects
- Exams
. . .
Attendance is expected (if you are healthy!)
Textbook
Beyond Multiple Linear Regression by Paul Roback and Julie Legler
- Available online
- Hard copies available for purchase
Textbook 2
The secondary text is: An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani – it is freely available online. Chapter 7.
- Hard copies available for purchase
Using R / RStudio
- RStudio Server is installed and should be used
- http://turing.cornellcollege.edu:8787/
Activities & Assessments
Readings
- Primarily from Beyond Multiple Linear Regression
- Recommend reading assigned text before lecture
. . .
Homework - Primarily from Beyond Multiple Linear Regression - Individual assignments - Work together but must complete your own work. Discuss but don’t copy.
Activities & Assessments
Mini-projects
Examples:
- Mini-project 01: Focused on models for non-normal response variables, such as count data
- Mini-project 02: Focused on models for correlated data
. . .
- Short write up and short presentation
- Team-based
Exams
Two exams this block, September 6th and 18th.
Each will have two components
- Component 1 will be on these dates and you will get a choice of oral or written format.
- Component 2 will be a take-home, open-book, open-note, exam.
- You will have 12 hours or more to complete this component.
Grading
Final grades will be calculated as follows
| Category | Points |
|---|---|
| Homework | 200 |
| Participation | 100 |
| Labs and Mini Projects | 300 |
| Exams | 400 |
| Total | 1000 |
See Syllabus on website for letter grade thresholds.
Resources
- Office hours to meet with your instructor in West 311
- Typically MWTh 3:05pm-4:05pm and by appt.
- Double check course calendar
- Make appointments by going to https://calendar.app.google/Li1dftFBXqzRnaX69
- Email Tyler George for private questions regarding personal matters or grades.
- Please put STA 363 in the subject line since I am also teaching capstone this semester
- College support at https://stats-tgeorge.github.io/personal_website/course-support.html.